Energy Cost Optimization for Water Distribution Networks Using Demand Pattern and Storage Facilities

sustainability Article Energy Cost Optimization for Water Distribution Networks Using Demand Pattern and Storage Facilities Yungyu Chang 1 , Gyewoon Choi 2 , Juhwan Kim 3 and Seongjoon Byeon 4, * 1 2 3 4 * Seohae Environmental Science Institute, Jeonbuk 54817, Korea; ravage@nate.com Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea; gyewoon@inu.ac.kr K-Water Institute, Water Research Center, Daejeon 34045, Korea; juhwan@kwater.or.kr International Center for Urban Water Hydroinformatics Research & Innovation, Incheon 21999, Korea Correspondence: seongjoon.byeon@gmail.com; Tel.: +82-32-851-5731; Fax: +82-32-851-5730 Received: 14 March 2018; Accepted: 6 April 2018; Published: 9 April 2018



 Abstract: Energy consumption in water supply systems is closely connected with the demand for water, since energy is mostly consumed in the process of water transport and distribution, in addition to the energy that might be needed to pump the water from its sources. Existing studies have been carried out on optimizing the pump operations to attain appropriate pressure and on controlling the water level of storage facilities to transfer the required demand and to reduce the energy cost. The idea is to reduce the amount of the water being supplied when the unit price of energy is high and to increase the supply when the unit price is low. To realize this scheme, the energy consumption of water supply systems, the amount of water transfer, the organization of energy cost structure, the utilization of water tanks, and so forth are investigated and analyzed to establish a model of optimized water demand management based on the application of water tanks in supplied areas. In this study, with the assumption that energy cost can be reduced by the redistribution of a demand pattern, a numerical analysis is conducted on transferring water demand at storage facilities from the peak energy cost hours to the lower energy cost hours. This study was applied at the Bupyeong 2 reservoir catchment, Incheon, Korea. Keywords: energy costs; genetic algorithms; water pipe network analysis; water storage facilities; demand pattern optimization; water distribution systems 1. Introduction These days, it is always necessary to make big efforts to save energy costs. In addition, energy savings in South Korea are always an important matter. Nationwide, South Koreans were reminded of this issue after suffering traumatic damage from a major blackout throughout the country in 2011. In addition, energy issues relating to climate change and emission reductions are currently treated as part of the national competitiveness strategy (IEA, Paris, France, 2012) [1]. In terms of water distribution networks (WDNs), water saving strategies (e.g., water demand control and water efficient design) are generally implemented as part of energy saving efforts by water saving company projects, water saving utilities, and different campaigns. These programs are favorably progressing since water use is naturally connected with energy consumption. Previous studies on demand management have established various data sets on water consumption patterns for different purposes of water use. Furthermore, several strategies have emerged and have been tested in previous studies [2–7]. In addition, Kim et al. (2007) and Kanakoudis and Tsitsifli (2013) monitored water demand patterns and their side effects in terms of hours in Sustainability 2018, 10, 1118; doi:10.3390/su10041118 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 1118 2 of 19 the day, days in the week, season, and weather [8,9]. Myeong et al. (2011) studied water demand with spatial patterns, such as the house type, number of rooms in the house, house area, and population in the house, by statistical analysis [10]. In addition, Kim et al. (2012) studied leakage as one of the reasons for energy overconsumption and developed a strategic model of leakage detection by demand pattern analysis [11]. Kanakoudis and Gonelas (2016) studied the economic leakage level in a water distribution system [12], and the same authors (2015) studied the joint effect of cost and water pressure. Seo (2011) developed a water savings index to evaluate strategies driven by local authorities and Kanakoudis and Gonelas (2015) studied water price changes based on pressure management [13,14]. Several preliminary studies on water storage facilities have been conducted. Previous studies have mainly focused on assessing storage facilities throughout the country [15,16]. The results have provided data on water tanks and their hydraulic characteristics, downtime, and water quality. Generally, the downtime averaged 0.3–3.9 days, and generally only 37.6% of total storage capacity was used. Most of the studies on WDNs have focused on water safety (quality), stable distribution (quantity), future water demand estimates, and efficient management [17–20]. To date, however, the primary subjects of the current study (e.g., control of water demand patterns and energy savings) have not been extensively researched. The major purpose of the current study is to develop a strategy of controlling the hourly patterns of water consumption by using storage facilities in order to reduce water use during high energy price hours (peak time) by distributing water use from peak times to low energy price hours. The purpose and scope of this study include the following: (a) investigate storage facilities in South Korea and especially Incheon, which is a metropolitan city; (b) develop a MATLAB (MATLAB R2013a, MathWork, Natick, MA, USA) optimization model with a genetic algorithm (GA) for energy cost minimization; (c) apply an EPANET (EPANET 2.0, US EPA, Cincinnati, OH, USA) model to assess WDNs; and (d) apply an EPANET model as a variable of a MATLAB optimization model to finalize the results. 2. Materials and Methods 2.1. Energy Consumption Generally, energy costs reflect geomorphological conditions from the source to the consumers; however, energy consumption in WDNs varies in location and process [21]. For instance, most energy consumption at a water treatment plant (WTP) is for facility operation, as it uses gravity-dominant flow, while water distribution pipelines use most of the energy for pumping using pressure flow. Energy consumption at a WTP will remain unconditionally compulsory and uncontrollable until new energy efficient machines are developed. However, pumping energy can be varied and reduced by its cost optimization. In addition, pumping energy, which uses electricity, incurs variable costs by the season and hour of the day; therefore, it is necessary to understand the electricity price system when investigating how to minimize the energy cost [22,23]. It is essential to secure stable electricity in the daytime as higher consumption is needed, and this phenomenon is connected with high-cost materials for power generation and high costs for energy consumption [24]. For example, in South Korea, unit cost per kilowatt-hour (Kw h) varies from 0.059 US dollar (USD) to 0.193 USD depending on the time of day (the cost is approximately 3.25 times higher at peak versus low usage times), and it could be possible to reduce around 69.2% of the energy cost by optimization. Table 1 shows an example of the energy consumption at the peak, intermediate, and low cost levels of water supply systems. According to the characteristics of the price of electricity, it is necessary to consider the hour of the day, demand distribution, and storage facility type (tank or reservoir). In terms of the hour of day, the peak hour varies depending on the season, and hourly energy consumption patterns also vary according to the season. However, seasonal water consumption patterns are similar to one another unless demand patterns are properly managed for energy cost savings by using a storage facility, such as a water storage tank at an apartment complex. The storage facility could be filled with water during low-cost hours and could supply water during high-cost hours from the facility Sustainability 2018, 10, 1118 3 of 19 without (or partially) using the main water supply [9]. Furthermore, WDNs with different WTPs might have distinct differences, and each WDN has its own storage facilities. Therefore, energy cost can be minimized through optimal operations of demand pattern control, WTPs, and storage facilities [25,26]. Although the price of water supply does not vary with time, the minimized, by optimization, energy cost can affect the improvement of water pricing policy [27]. Table 1. Energy consumption at a water supply system (example of South Korea). Year Cost Level Unit Price US Dollar/kW h Intake St. (%) Pumping St. (%) Water distribution Networks (WDNs) (%) 2011 High Middle Low 0.193 0.112 0.059 47.9 35.5 16.6 47.9 35.5 16.6 46.2 36.8 17.0 2012 High Middle Low 0.193 0.112 0.059 47.0 35.5 17.5 47.2 35.4 17.4 45.1 36.7 18.2 2.2. Water Storage Facilities Water supply by WDNs is occasionally operated by different supply methods, such as (1) direct water supply (i.e., water coming directly from the water supply); (2) indirect water supply (i.e., through storage facilities such as a tank); and (3) combinations of direct-indirect water supplies. In general, storage facilities are used to ensure a stable water supply with constant water supply through securing water pressure. Table 2 presents the current state of installed water storage facilities in Incheon metropolitan city. Table 2. Water distribution storage facilities (water tank) in Incheon city. Building Type and Minimum Criteria of Water Storage Facility Necessity No. of Storage Facilities General buildings larger than 5000 m2 Commercial buildings larger than 3000 m2 Complex buildings lager than 2000 m2 Theaters Private academies over 2000 m2 Large shops at individual buildings Wedding halls over 2000 m2 Indoor gyms Apartment complexes Total 816 334 450 2 2 6 12 4 1067 2693 Most of the storage facilities are installed in the apartments or in large buildings with areas greater than 2000 m2 . The storage facilities are operated by individual buildings, and there is no special code on storage facility operations; therefore, the operation of facilities occasionally depend on the practically based rule of meeting water demand. Kwak et al. (2005) studied the hydraulic characteristics of water storage facilities [16]. In their study, downtime averaged 0.3–3.9 days and the water level average was 37.6% of height. At this level, and operating inefficiently, less than 40% of the storage facility is being used. However, water demand is typically less than 40% of the capacity of a water storage facility, and the rest (60% of storage) of the capacity can be used as research capacity for this study. 2.3. EPANET Since the EPANET model was developed by the US Environment Protection Agency in 1994, hydraulic simulations of WDNs have been routinely implemented. EPANET has been extensively used Sustainability 2018, 10, 1118 4 of 19 as a tool for research implementation in terms of network design optimization [28,29]. EPANET can be used through a graphical user interface (GUI) as standalone software or can be used through a toolkit library using open source user-specific software. The library and source code can be called through external programming language [29]. In this study, the MATLAB toolkit for the EPANET model was used [30]. MATLAB allows connections to external software libraries, which can help researchers use tools and simulators developed originally in a different language. The EPANET source code was originally formulated in C language. 2.4. Genetic Algorithms GAs are widely used for the optimization of water-related problems [28,31–33]. This optimization technique searches for a solution with minimum costs given as the objective goal of the target problem [33]. Prasad et al. (2003) presented a GA with multivariable functions for an optimal design of WDNs using a Pareto-optimal approach [34]. Prasad and Park (2004) outlined a multi-objective GA for an optimal WDN design [35]. Deb and Jain (2014) developed a nondominated sorting approach for Multi-objective Genetic Algorithm (MOGA) for multi-objective optimization [36]. The considered objectives minimize the error among real and simulated values and maximize reliability. In this study, a GA was selected and coded by simple revision. The GA, based on Deb and Jain’s (2014) work [36], included the following considerations: (1) minimum and maximum values for mutation and cross-over; (2) the EPANET model as its variable; (3) many variables with multiple objectives; and a (4) simultaneously fixed bug at mutation with 10+ variables. The GA model was applied for emitter calibration by minimizing errors because the number of variables to be calibrated in WDN was the same as the number of junctions in the network. The EPANET model was applied as the general solver for variables to be calibrated in conjunction with GA. The Pareto front (known as the nondominated solution set) is the fully optimal solution in a multi-objective sense, and the GA is a robust optimization approach for multi-objective purposes with the Pareto front [37]. 3. Development of the Optimization Tool 3.1. Objective Function A formula was developed based on the concept of energy costs for water transfer in WDNs. Energy costs are closely related to water flow and its unit energy cost, and water flow is generated based on water consumption, which has occasionally similar patterns in terms of the energy cost hourly patterns. A possible assumption is that the energy cost could be minimized by redistributing pattern within the concept of GA optimization. The basic concept for optimization is described in Figure 1. As shown in Figure 1, the demand pattern is considered as a variable, and they become input variables for EPANET to produce the variation of water flow rate. Produced water flow rate as an hourly pattern becomes a new water consumption pattern, and the calculation is iterated through GA optimization. In addition, the energy cost by each pattern is calculated, and the iteration continues until the tool finds the minimum cost as specified. The water consumption at peak cost time can be classified by consumption through a direct water supply or an indirect water supply (by tank). A direct water supply cannot control the consumption pattern, while an indirect water supply can control it by storage operation. A water supply with high-cost pumping energy during peak price hours can be moved to low-cost hours through tank storage operations. Thus, the daily water supply can be expressed through the summation of a controllable water supply and an uncontrollable water supply, and the energy cost is the summation of hourly water supply multiplied by hourly energy cost, as depicted in Equations (1) and (2): 24 Qday = ∑ [Qn · Pn (i) + Qu · Pu (i)], i =1 (1) Sustainability 2018, 10, 1118 5 of 19 24 Energy Cost = ∑ [Qn · Pn (i) + Qu · Pu (i)]·Ecost (i), (2) i =1 where Qday is the daily total water consumption, Qn is the daily water consumption by direct water supply (without a storage facility), Qu is the daily water consumption by the indirect water supply (with a water storage facility), Pn (i ) is the dimensionless pattern of water consumption for the direct water supply (uncontrollable) at time step i and can be determined through hourly water consumption by direct water supply divided by the Qn , Pu (i ) is the dimensionless pattern of water consumption for the indirect water supply (controllable) at time step i and can be determined by hourly water consumption (indirect with a water storage facility) divided by Qu , and Ecost (i ) is the energy cost for water transfer at time step i. Pn and Pu are dimensionless hourly patterns of daily water consumption and they vary within a range from zero to one and the sum of 24 h value should be one, respectively. In Equations (1) and (2), Qn and Qu are fixed daily value and Pn is also a fixed value; therefore, Qn , Qu and Pn are variables out of design space while Pu is a variable that varies within the range from zero to one. In addition, unit cost of electricity also varies with time and season. Therefore, the water consumption designed by the Pu and the energy cost designed by the unit price of electricity were used as the bi-objective Pareto front objective function. By Equations (1) and (2), the objective function for optimization can be defined as Equation (3): 24 Objective Function f or Minimum cost (OF ) = Minimize ∑ Di Ci , (3) i =1 where Di is the water consumption at time step i, and Ci is the unit price of electricity at time step i. The energy cost optimization should also consider the stability of water consumption by securing the necessary water pressure; therefore, there must be two constraints. In the optimization model, we tried to find the lowest objective function value as the energy cost for the water supply through designing a Pu variable. However, some of chromosomes that are produced through GA can lead to unexpected or infeasible values. Previous studies on GA recommend defining a penalty for them to guide algorithms to avoid infeasible values through defining penalty functions [38,39]. Therefore, the first constraint applies a penalty function throughout the WDNs at junctions, and the other constraint defines the pressure range constraint at specified junctions. Therefore, the penalty function is defined as Equation (4): 24 Penalty Function ( PF ) = α· ∑ Pt (i ), (4) i =1 where Pt (i ) is the pressure at all junctions within total WDNs, and α is a coefficient to determine relativeness among OF and PF. The value of α is zero when the pressure at the junction is in the range of specified minimum and maximum pressure by user specification. Thus, the OF can be revised within the consideration of PF as Equation (5): OF = 24 24 i =1 i =1 ∑ Di Ci + α· ∑ Pt (i). (5) Sustainability 2018, 9, x FOR PEER REVIEW 6 of 19 24 𝑂𝑏𝑗𝑒𝑐𝑡𝑖𝑣𝑒 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑓𝑜𝑟 𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑐𝑜𝑠𝑡 (𝑂𝐹) = 𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 ∑ 𝐷𝑖 𝐶𝑖 , Sustainability 2018, 10, 1118 Sustainability 2018, 9, x FOR PEER REVIEW 𝑖=1 (3) 6 of 19 5 of 19 where 𝐷𝑖 is the water consumption at time step 𝑖, and 𝐶𝑖 is the unit price of electricity at time step 𝑖. The energy cost optimization should also consider the stability of water consumption by securing the necessary water pressure; therefore, there must be two constraints. In the optimization model, we tried to find the lowest objective function value as the energy cost for the water supply through designing a 𝑃𝑢 variable. However, some of chromosomes that are produced through GA can lead to unexpected or infeasible values. Previous studies on GA recommend defining a penalty for them to guide algorithms to avoid infeasible values through defining penalty functions [38,39]. Therefore, the first constraint applies a penalty function throughout the WDNs at junctions, and the other constraint defines the pressure range constraint at specified junctions. Therefore, the penalty function is defined as Equation (4): 24 𝑃𝑒𝑛𝑎𝑙𝑡𝑦 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛 (𝑃𝐹) = 𝛼 ∙ ∑ 𝑃𝑡 (𝑖), (4) 𝑖=1 where 𝑃𝑡 (𝑖) is the pressure at all junctions within total WDNs, and 𝛼 is a coefficient to determine relativeness among OF and PF. The value of 𝛼 is zero when the pressure at the junction is in the range of specified minimum and maximum pressure by user specification. Thus, the OF can be revised within the consideration of PF as Equation (5): 24 24 𝑂𝐹 = ∑ 𝐷𝑖 𝐶𝑖 + 𝛼 ∙ ∑ 𝑃𝑡 (𝑖). Figure 1. 𝑖=1 𝑖=1 General framework for energy (5) cost optimization. The water 3.2. Study Study Area consumption at peak cost time can be classified by consumption through a direct 3.2. Area water supply or an indirect water supply (by tank). A direct water supply cannot control the In this this study, study,the thestudy studyarea areaisischosen chosenin inthe theeffluence effluencearea areaof ofBupyeong’s Bupyeong’ssecond second water water supply supply In consumption pattern, while an indirect water supply can control it by storage operation. A water reservoir since there are several different types of storage facilities and different types of land use reservoir since there are several different types of storage facilities and different types of land use supply with high-cost pumping energy during peak price hours can be moved to low-cost hours (e.g., residential, residential, commercial, commercial, public, public, and and an an apartment apartment complex). complex). In addition, addition, data about WDNs, WDNs, (e.g., through tank storage operations. Thus, the daily water supply can be expressed through the electricity,and andhydraulic hydraulic data (flow rate pressure) and pressure) could be secured. the electricity, data (flow rate and could be secured. Figure 2aFigure shows 2a theshows boundary summation of a controllable water supply and an uncontrollable water supply, and the energy cost boundary and land the study area, and Figurethe 2bpipeline shows the on the block. and land use data ofuse the data studyofarea, and Figure 2b shows onpipeline the block. is the summation of hourly water supply multiplied by hourly energy cost, as depicted in Equations (1) and (2): 24 𝑄𝑑𝑎𝑦 = ∑[𝑄𝑛 ∙ 𝑃𝑛 (𝑖) + 𝑄𝑢 ∙ 𝑃𝑢 (𝑖)], (1) 𝑖=1 24 𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑠𝑡 = ∑[𝑄𝑛 ∙ 𝑃𝑛 (𝑖) + 𝑄𝑢 ∙ 𝑃𝑢 (𝑖)] ∙ 𝐸𝑐𝑜𝑠𝑡 (𝑖), (2) 𝑖=1 where 𝑄𝑑𝑎𝑦 is the daily total water consumption, 𝑄𝑛 is the daily water consumption by direct water supply (without a storage facility), 𝑄𝑢 is the daily water consumption by the indirect water supply (with a water storage facility), 𝑃𝑛 (𝑖) is the dimensionless pattern of water consumption for the direct water supply (uncontrollable) at time step 𝑖 and can be determined through hourly water consumption by direct water supply divided by the 𝑄𝑛 , 𝑃𝑢 (𝑖) is the dimensionless pattern of water consumption for the indirect water supply (controllable) at time step 𝑖 and can be determined by hourly water consumption (indirect with a water storage facility) divided by 𝑄𝑢 , and 𝐸𝑐𝑜𝑠𝑡 (𝑖) is the energy cost for water transfer at time step 𝑖. 𝑃𝑛 and 𝑃𝑢 are dimensionless hourly patterns of daily water consumption and they vary within a range from zero to one and the sum of 24 h value should be one, respectively. In Equations (1) and (2), 𝑄𝑛 and 𝑄𝑢 are fixed daily value and 𝑃𝑛 is also a fixed value; therefore, 𝑄𝑛 , 𝑄(a) 𝑃𝑢 is a variable that varies 𝑢 and 𝑃𝑛 are variables out of design space while (b) within the range from zero to one. In addition, unit cost of electricity also varies with time and season. Figure2. 2. Maps Maps of of study study area: area: (a) (a) land land use use map; map; and and (b) (b)pipeline pipelinemap. map. Figure Therefore, the water consumption designed by the 𝑃𝑢 and the energy cost designed by the unit price of electricity were used as the bi-objective Pareto front objective function. By Equations (1) and (2), the objective function for optimization can be defined as Equation (3): Sustainability 2018, 10, 1118 Sustainability 2018, 9, x FOR PEER REVIEW 7 of 19 7 of 19 In In the the study study area, the daily maximum water supply is 46,000 m33/day, /day, and and the the water water supply supply is is generally generally coming coming from from Bupyeong’s Bupyeong’s second second water watersupply supplyreservoir, reservoir, which which has has 20,000 20,000 m m33of ofcapacity, capacity, 59.5 59.5 m m at the high-water high-water level, and 55 m at the low-water level. The The maximum maximum water water use use pattern pattern occurs 7:00 p.m., andand the the water use pattern for the cost time occurs between between6:00 6:00p.m. p.m.and and 7:00 p.m., water use pattern forhigh the energy high energy costframe time (peak and theand intermediate high cost time frame hours) high is while pattern for the framehours) (peak hours) the intermediate high cost time(mid frame (mid is hours) highthe while the pattern low energy cost time (low (low hours) is low. Thus, it isit possible to tolower for the low energy costframe time frame hours) is low. Thus, is possible lowerenergy energycosts costs by by redistributing redistributing the water use pattern. The The water use pattern is shown in Figure 3. Figure 3. 3. Water Water demand demand pattern pattern of of the the study study area. Figure In Figure 3, 3,the thewater waterdemand demand pattern is high during peak intermediate cost hours, In Figure pattern is high during peak costcost and and intermediate cost hours, while while the pattern low during low energy cost In hours. In thiswe study, we assumed high water the pattern is low is during low energy cost hours. this study, assumed that the that highthe water demand demand pattern can be moved the low cost hours using the water storage facilities pattern can be moved to the low to energy cost energy hours by using the by water storage facilities (which are not (which are not actively used), and the use of facility can be determined by the optimization process. actively used), and the use of facility can be determined by the optimization process. The facilities and 11,321 m3 m of3 capacity in total storage facilities. The The study studyarea areahas has122 122storage storage facilities and 11,321 of capacity in total storage facilities. total capacity of the storage facilities is approximately 24% of the daily maximum water supply The total capacity of the storage facilities is approximately 24% of the daily maximum water supply 3 3 3 of capacity, (46,000 Seven storage facilities have greater thanthan 500 m capacity, and they about (46,000 m m3/day). /day). Seven storage facilities have greater 500ofm andrepresent they represent half thehalf total of all the storage facilities, as shown in Table about thecapacity total capacity of all the storage facilities, as shown in 3. Table 3. Table Table 3. 3. Number Number of of storage storage facilities facilities and and their their water water demand demand in in study study area. area. Storage Capacity Storage Capacity (m3 ) (m3) 1–10 1–10 11–50 11–50 51–100 51–100 101–500 101–500 501–1000 501–1000 Total Total No. ofof Facilities No. Facilities 1818 7070 1414 1313 7 7 122 122 Sum of Daily Mean Water Demand Sum of Daily Mean Water Demand (m3 /day) (m3/day) 106106 1718 1718 890890 2976 2976 5631 5631 11,321 11,321 The official official GIS GIS provided provided from from Incheon Incheon city city authorities authorities was was used used as as the themajor majorinput inputfor forEPANET EPANET The modeling, and useuse pattern datadata was included in the model an extended simulation. modeling, andthe thewater water pattern was included in the for model for an period extended period The completed set of EPANET data was validated by test simulation and added as a variable of Matlab simulation. The completed set of EPANET data was validated by test simulation and added as a GA optimization. 4 shows the Table summarized for GAparameters optimization. variable of Matlab Table GA optimization. 4 showsparameters the summarized for GA optimization. The parameters were determined through several test implementations. Moreover, the main Table 4. Main parameters genetic algorithm variable for optimization was the WDN’s pipefor network problem,optimization. which was also quite complicated to solve. The length of chromosome was set as 24 since there were 24 real number coding for each Procedure Parameter Name Value Remarks hour pattern (24/day). The population was set as 50 after several practice runs. The linear ranking Real number coding Length of chromosome L = 24 was selected as 1.7 for higher selective pressure. In addition,50random selection for crossover and a Population Population 0.1 mutation ratio were selected. ηmax = 1.7 Linear ranking Crowding degree of crossover Sustainability 2018, 10, 1118 8 of 19 Table 4. Main parameters for genetic algorithm optimization. Procedure Parameter Name Value Remarks Real number coding Length of chromosome L = 24 Population Population 50 Linear ranking Crowding degree of crossover ηmax = 1.7 Crossover Crossover ratio 0–50% Randomly varying in generations Mutation Maximum generation 0–10% Randomly varying in generations Scaling window Scaling window Ws = 1.0 Elitist selection Survived generation 1 The model simulation consisted of five different cases for each season. The first case shows the current state of operation without any application of energy saving strategies. The second, third, and fourth cases depict the redistribution of water demand to the low energy cost hours at 60%, 80%, and 100% of water use, respectively. The last case applies the GA optimization. Table 5 shows detailed descriptions of the simulation cases. Table 5. The classification of cases. Season Case Name Description Summer SP S1 S2 S3 SGA Current State Uniform distribution of 60% demand of storage facility at low price hours Uniform distribution of 80% demand of storage facility at low price hours Uniform distribution of 100% demand of storage facility at low price hours Distribution optimization by genetic algorithm Winter WP W1 W2 W3 WGA Current State Uniform distribution of 60% demand of storage facility at low price hours Uniform distribution of 80% demand of storage facility at low price hours Uniform distribution of 100% demand of storage facility at low price hours Distribution optimization by genetic algorithm Spring and Autumn FP F1 F2 F3 FGA Current State Uniform distribution of 60% demand of storage facility at low price hours Uniform distribution of 80% demand of storage facility at low price hours Uniform distribution of 100% demand of storage facility at low price hours Distribution optimization by genetic algorithm 4. Results and Discussion 4.1. Model Sensitivity and Convergence To choose the value of α, a coefficient to determine relativeness among objective function and penalty function as described in Equation (4), the sensitivity of α was examined. The result is shown in Figure 4. The pattern of Pu (i ) (the dimensionless pattern of water consumption for the indirect water supply, which is controllable, at time step i) in Figure 4 is similar to all cases since the GA is calculated based on the rank of the generated population, and the extreme values in the population are excluded due to their low rank. However, the higher value of α distributes homogeneous pressure, which could provide less fluctuation since the penalty function reflects the pressure in the pipe network. In addition, the simulation time, from start to convergence, with the value of higher α was shorter than lower or the zero value of α. To summarize, the penalty function affected the convergence in determining the optimized value of the objective function; however, the sensitivity itself was not high. The value of α Sustainability 2018, 10, 1118 9 of 19 was determined as 10% by the analysis. The convergence of the objective function with the value of α as 10% is shown Figure Sustainability 2018, 9,in x FOR PEER5.REVIEW 9 of 19 Sustainability 2018, 9, x FOR PEER REVIEW 9 of 19 Figure 4. 4. Demand pattern pattern change by by the the penalty penalty function function coefficient. coefficient. Figure Figure 4.Demand Demand pattern change change by the penalty function coefficient. Figure 5. Convergence of objective function value with summer season data. Figure 5. Convergence of objective function value with summer summer season season data. data. The convergence was tested with the result of the objective function value in the summer season. The convergence convergence was was tested tested with with the the result of function value in summer season. The of the the objective objective valuecan in the the summer season. The convergence was completed at the result 22nd generation. Fast function convergence show the lack of The convergence convergence was was completed completed at at the the 22nd 22nd generation. generation. Fast convergence can show the lack of of The Fast convergence can show the lack diversity; however, this fast convergence could be made by the effect of the penalty function, which diversity; however, this fast convergence could be made by the effect of the penalty function, which diversity; however, thisexploration fast convergence could be made thetotal effect of the of penalty function,with which can provide a strong capacity for the model.by The number pipe networks caniterations providewas strong exploration capacity for the the model. model. The total number number of can pipebenetworks networks with can provide aa strong exploration for total of pipe with 1100 times, and thecapacity convergence made a slowThe improvement, which appropriate iterations was 1100 1100 times, times, and the the convergence made slow improvement, which cansimilar be appropriate appropriate iterations was and convergence made aa slow which can be for optimization. In addition, the convergence patterns in improvement, the other seasons were to the for optimization. In addition, the convergence patterns in the other seasons were similar to the of the In summer season. forexample optimization. addition, the convergence patterns in the other seasons were similar to the example example of the summer season. To optimize the energy cost, we examined cases by uniform distribution and optimization in of the summer season. To optimize the energy cost, cost,spring we examined examined cases by uniform uniform distribution and optimization optimization in different seasons; the and autumn seasons were treated as the same case since the in To optimize thehowever, energy we cases by distribution and different seasons; however, the spring and autumn seasons were treated as the same case since the energy cost in the spring and autumn are the same. The classification of cases is shown in Table 5. different seasons; however, the spring and autumn seasons were treated as the same case since the energy cost cost in in the the spring spring and and autumn autumn are are the the same. same. The The classification classification of of cases cases is is shown shown in in Table Table5.5. energy 4.2. Summer Season 4.2. Summer Season In the summer season, the flow rate between 10:00 a.m. and 11:00 p.m. (peak and mid hours) is high, while water consumption for the low energy 10:00 cost hours comparably as shown in Figure In the summer season, the flow rate between a.m. is and 11:00 p.m.low, (peak and mid hours) is 6. while water consumption for the low energy cost hours is comparably low, as shown in Figure high, 6. Sustainability 2018, 10, 1118 10 of 19 4.2. Summer Season In the summer season, the flow rate between 10:00 a.m. and 11:00 p.m. (peak and mid hours) is high,Sustainability while water for the low energy cost hours is comparably low, as shown in Figure 6. 2018,consumption 9, x FOR PEER REVIEW 10 of 19 Figure 6. Resulting comparison for the summer season. Figure 6. Resulting comparison for the summer season. In Case S1, water consumption from the storage facilities was increased during the low energy In S1,while water consumption from facilities increased during the low energy costCase hours, it decreased during peakthe andstorage mid hours. Biggerwas variations occurred in Case S2 and S3, withwhile an almost reversed pattern current In addition, the results occurred of S1, S2, and S3 S2 cost hours, it decreased duringfrom peaktheand mid state. hours. Bigger variations in Case showed scales of variations by the different proportions usage showed and S3, withdifferent an almost reversed pattern from the current state. of Instorage addition, thebut results ofsimilar S1, S2, and variation patterns since the distribution method was almost the same. In the case with S3 showed different scales of variations by the different proportions of storage usage but GA showed optimization (SGA), the pattern was between the patterns of S1 and S2 during the low energy cost similar variation patterns since the distribution method was almost the same. In the case with GA hours, while the pattern was similar to the S3 pattern during peak hours. It is clear that the water optimization (SGA), the pattern was between the patterns of S1 and S2 during the low energy cost demand during peak hours and mid hours was transferred and distributed for low cost hours, as hours, while the pattern was similar to the S3 pattern during peak hours. It is clear that the water shown in Table 6. demand during peak hours and mid hours was transferred and distributed for low cost hours, as shown in Table 6. Table 6. Hourly variation of flow rate patterns for the summer season. Time 1 2 3 4 5 6 7 8 9 Table 6. Hourly variationHourly of flowFlow rate patterns for the summerDifference season. with 3 3 Rate (m /h) Current S1 S2 S3 SGA Hourly1593 Flow Rate (m3 /h) 1 1563 1819 2046 1363 Cost Level 2 1422 S1 1436 S2 1662 S3 1889 SGA1409 Current 3 1355 1358 1584 1810 1232 1563 1593 1819 2046 1363 4 996 1164 1390 1617 1151 1422 1436 1662 1889 1409 5 Low 777 1179 1405 1631 1237 1355 1358 1584 1810 1232 6 767 1197 1423 1649 895 996 1164 1390 1617 1151 7 905 1305 1531 1758 1542 777 1179 1405 1631 1237 Low 8 1120 1491 1718 1944 1674 767 1197 1423 1649 895 9 1441 1738 1964 2191 1918 905 1305 1531 1758 1542 10 1761 1497 1335 1173 1552 Mid 1120 1491 1718 1944 1674 11 1854 1538 1376 1215 1577 1441 1738 1964 2191 1918 12 Peak 1794 1574 1412 1251 1255 13 Mid 1799 1568 1406 1244 1465 14 1634 1598 1436 1274 1397 15 1535 1493 1331 1169 1338 Peak 16 1567 1451 1289 1127 1247 17 1584 1403 1241 1080 1169 18 Mid 1500 1402 1241 1079 1206 Time Cost Level Current (m ) S1 S2 S3 SGA 3 Difference with−483 Current200 (m ) −30 −257 −240 −466 13 SGA S1−13 S2 S3 −3 −229 −456 123 −30 −257 −483 200 −168 −394 −621 −155 −13 −240 −466 13 −401 −628 −854 −460 −3 −229 −456 123 −430 −656 −883 −128 −168 −394 −621 −155 −400 −626 −853 −637 −401 −628 −854 −460 −371 −598 −824 −554 −430 −656 −883 −128 −297 −523 −749 −477 −400 −626 −853 −637 264 426 587 209 −371 −598 −824 −554 316 478 640 278 −297 −523 −749 −477 220 382 543 539 232 394 555 334 37 198 360 238 42 204 365 197 117 278 440 320 181 342 504 415 97 259 421 294 Sustainability 2018, 10, 1118 11 of 19 Table 6. Cont. Hourly Flow Rate (m3 /h) Time Cost Level 10 11 Difference with Current (m3 ) Current S1 S2 S3 SGA S1 S2 S3 SGA Mid 1761 1854 1497 1538 1335 1376 1173 1215 1552 1577 264 316 426 478 587 640 209 278 12 Peak 1794 1574 1412 1251 1255 220 382 543 539 13 Mid 1799 1568 1406 1244 1465 232 394 555 334 Peak 1634 1535 1567 1584 1598 1493 1451 1403 1436 1331 1289 1241 1274 1169 1127 1080 1397 1338 1247 1169 37 42 117 181 198 204 278 342 360 365 440 504 238 197 320 415 14 15 16 17 18 19 20 19 Mid 21 20 22 21 23 22 24 23 Low 24 LowLow Summary MidLow Summary PeakMid Peak 1500 1402 1552 1402 1575 1552 1667 1575 1641 1667 1460 1641 1460 11,806 11,806 14,752 8114 14,752 8114 1402 1241 1079 1206 97 259 1434 1272 1110 1516 −32 130 1481 1320 1158 1853 71 232 1434 1272 1110 1516 −32 130 1472 1310 1148 1626 103 265 1481 1320 1158 1853 71 232 1387 1225 1063 1767 281 442 1472 1310 1148 1626 103 265 1359 1197 1036 1500 282 444 1387 1225 1063 1767 281 442 1556 1359 17821197 2009103617841500 −96 282 −322 444 1556 16,279 178218,544 200914,265 1784−2209−96 −4474 −322 14,015 14,01511,682 16,27910,227 18,544 14,2651614 −2209 3070 −4474 13,138 13,751 7518 13,138 6709 11,6825901 10,227665513,751 5961614 1404 3070 7518 6709 5901 6655 596 1404 Sustainability 2018, 9, x FOR PEER REVIEW 421 292 394 292 427 394 604 427 606 604 −606 549 −−549 6738 −6738 4525 2213 4525 2213 294 −114 −301 −114 −51 −301 −99 −51 141 11 of 19 −99 −324 141 −324 −2459 −2459 1000 1459 1000 1459 As shown in Table 6, the transferred water from intermediate hours to low hours is higher in As shown in Table 6, the transferred water from intermediate hours to low hours is higher in cases S1, S2, and S3 than for SGA, while the transferred water from peak hours to low hours is higher cases S1, S2, and S3 than for SGA, while the transferred water from peak hours to low hours is higher in in thethe SGA case in the thecases casesfor forthe thesummer summer season, SGA SGA casethan thanininthe therest restof ofthe thecases. cases. Therefore, Therefore, in season, thethe SGA case shows higher efficiency for energy savings. Figure 7 shows the transferred water demand from case shows higher efficiency for energy savings. Figure 7 shows the transferred water demand from thethe peak and mid peak and midhours hourstotothe thelow lowhours. hours. Figure 7. Flow rate transferred to low cost hours by summer season simulation. Figure 7. Flow rate transferred to low cost hours by summer season simulation. In Figure 7, the water demand transferred from peak hours to low hours in cases S1, S2, and S3 was approximately 27–33%, while it was approximately 60% in the SGA case, as the GA tries to give priority for water demand during peak hours. The saving effect in case S1 and SGA was approximately 33.3%. 4.3. Winter Season Sustainability 2018, 10, 1118 12 of 19 In Figure 7, the water demand transferred from peak hours to low hours in cases S1, S2, and S3 was approximately 27–33%, while it was approximately 60% in the SGA case, as the GA tries to give priority for water demand during peak hours. The saving effect in case S1 and SGA was approximately 33.3%. 4.3. Winter Season In the winter season, unlike the summer, the flow rate is very high in the evening time, especially between 6:00 p.m. 9:00 p.m. and 10:00 p.m. and 11:00 p.m., as shown in Figure 8. Sustainability 2018, and 9, x FOR PEER REVIEW 12 of 19 Figure 8. Result comparison for the winter season. Figure 8. Result comparison for the winter season. In cases W1, W2, and W3, the variations of water demand pattern from the current state were In casestoW1, W2, for andthe W3, the variations of waterthe demand from statetowere similar the cases summer season. In addition, patternpattern revised by casethe W1current was similar the WGA case. W1, W2, and W3 at 11:00 p.m. were close to one another, with the highest variations similar to the cases for the summer season. In addition, the pattern revised by case W1 was similar to in thecase. day. Demand changes in the W1, W2,close and W3 casesanother, were similar thehighest results of the the WGA W1, W2,pattern and W3 at 11:00 p.m. were to one withtothe variations season in S1, S2,changes and S3, in respectively, withand theW3 reversed whiletothe caseof the in thesummer day. Demand pattern the W1, W2, cases demand, were similar theWGA results showed very low demand between 5:00 p.m. and 6:00 p.m. and at 11:00 p.m. It is clear that the water summer season in S1, S2, and S3, respectively, with the reversed demand, while the WGA case showed demand during the peak and mid hours was transferred and distributed to the low hours, as shown in very low demand between 5:00 p.m. and 6:00 p.m. and at 11:00 p.m. It is clear that the water demand Table 7. during the peak and mid hours was transferred and distributed to the low hours, as shown in Table 7. Table 7. Hourly variation of flow rate pattern for the winter season. Table 7. Hourly variation of flow rate pattern for the winter season. Hourly Flow Rate Difference with (m3/h) 3 Current (m3) HourlyW1 Flow Rate (m /h) Difference withW3 Current (m3 ) Current W2 W3 WGA W1 W2 WGA Cost Level 1 1573 W11924 W22150 W32376WGA 1923 W1 −351 −577 −804 Current W2 W3 −351WGA 2 1518 1876 2102 2329 1885 −357 −584 −810 −366 1573 1924 2150 2376 1923 −351 −577 −804 −351 3 1422 1793 2019 2246 1886 −371 −597 −823 −464 1518 1876 2102 2329 1885 −357 −584 −810 −366 4 1364 1744 1970 2197 1740 −380 −606 −833 −376 1422 1793 2019 2246 1886 −371 −597 −823 −464 5 Low 1436 1749 1975 2202 1605 −313 −539 −766 −169 1364 1744 1970 2197 1740 −380 −606 −833 −376 6 1326 1807 2033 2260 1994 −481 −708 −934 −668 1436 1749 1975 2202 1605 −313 −539 −766 −169 Low 7 1482 1958 2185 2411 2064 −477 −703 −929 −582 1326 1807 2033 2260 1994 −481 −708 −934 −668 8 2002 2135 2362 2588 1721 −133 −360 −586 281 1482 1958 2185 2411 2064 −477 −703 −929 −582 9 1858 2171 2398 2624 2105 −314 −540 −766 −248 2002 2135 2362 2588 1721 −133 −360 −586 281 10 1935 1817 1655 1493 2213 118 280 441 −278 1858 2171 2398 2624 2105 −314 −540 −766 −248 Mid 11 2130 1867 1705 1544 1671 263 425 587 459 12 Peak 1868 1809 1647 1486 1751 59 220 382 117 13 Mid 1921 1870 1708 1547 1971 51 213 374 −50 14 1898 1849 1687 1526 2016 49 210 372 −118 15 1904 1751 1590 1428 1572 153 315 477 333 Peak 16 1940 1701 1539 1378 1585 239 401 563 355 17 1700 1709 1547 1386 1599 −9 153 314 101 18 1722 1773 1611 1449 1482 −50 112 273 241 Mid 19 1880 1833 1672 1510 1907 47 209 371 −26 Time Time 1 2 3 4 5 6 7 8 9 Cost Level Sustainability 2018, 10, 1118 13 of 19 Table 7. Cont. Hourly Flow Rate (m3 /h) Time Cost Level 10 11 Difference with Current (m3 ) Current W1 W2 W3 WGA W1 W2 W3 WGA Mid 1935 2130 1817 1867 1655 1705 1493 1544 2213 1671 118 263 280 425 441 587 −278 459 12 Peak 1868 1809 1647 1486 1751 59 220 382 117 13 Mid 1921 1870 1708 1547 1971 51 213 374 −50 Peak 1898 1904 1940 1700 1849 1751 1701 1709 1687 1590 1539 1547 1526 1428 1378 1386 2016 1572 1585 1599 49 153 239 −9 210 315 401 153 372 477 563 314 −118 333 355 101 14 15 16 17 18 19 20 2120 2221 2322 23 24 24 1722 1773 1611 1449 1482 −50 112 273 241 13 of 19 1880 1833 1672 1510 1907 47 209 371 −26 2122 1898 1737 1575 1866 224 385 547 256 2122 1906 1898 17441737 158215751849 1866635 224 797 385 958 547 256 2541 692 2541 1906 1744 1582 1849 635 797 958 692 2379 1894 1733 1571 2,321 484 646 808 58 2379 1894 1733 1571 2,321 484 646 808 58 2454 1794 1632 1471 1,710 660 821 983 744 2454 1794 1632 1471 1,710 660 821 983 744 2349 2102 2328 2554 2,295 248 21 −205 54 2349 2102 2328 2554 2,295 248 21 −205 54 16,330 19,218 −2928−2928−5193 2888 16,330 19,258 19,25821,523 21,52323,787 23,787 19,218 −5193 −7457 −7457 −−2888 14,498 13,204 11,910 15,126 1720 3014 4308 1091 16,217 16,217 14,498 13,204 11,910 15,126 1720 3014 4308 1091 12,177 10,974 10,004 9034 10,386 1202 2173 3143 1791 12,177 10,974 10,004 9034 10,386 1202 2173 3143 1791 Sustainability 2018, 9, x FOR PEER REVIEW Summary Summary Mid Low Low Low Low Mid Mid Peak Peak As shown in Table Table 7, 7, the transferred transferred water water from from mid mid hours hours to low hours is higher in the W1, W2, and W3 water from peak hours to low hours is higher in the W3 cases casesthan thanthe theWGA, WGA,while whilethe thetransferred transferred water from peak hours to low hours is higher in WGA case case than than in thein rest the cases, similar the case summer Therefore, the WGA theofrest of thewhich cases, was which was to similar to for thethe case for theseason. summer season. in the cases in forthe thecases winterfor season, the WGA case the shows higher for energy savings. 9 Therefore, the winter season, WGA caseefficiency shows higher efficiency forFigure energy shows the transferred water demand from the peak and mid hours to the low hours. savings. Figure 9 shows the transferred water demand from the peak and mid hours to the low hours. Figure 9. 9. Flow transferred to to low low cost cost hours hours by by the the winter winter season season simulation. simulation. Figure Flow rate rate transferred In Figure 9, the water demand transferred from the peak hours to the low hours in cases W1, W2, and W3 was approximately 40%, while it was approximately 60% in the WGA case, as the GA tries to give priority for water demand during peak hours. The saving effect by cases W(1, 2, and 3) and WGA was approximately 33.3%. 4.4. Spring and Autumn Seasons Sustainability 2018, 10, 1118 14 of 19 In Figure 9, the water demand transferred from the peak hours to the low hours in cases W1, W2, and W3 was approximately 40%, while it was approximately 60% in the WGA case, as the GA tries to give priority for water demand during peak hours. The saving effect by cases W(1, 2, and 3) and WGA was approximately 33.3%. Sustainability 2018, 9, x FOR PEER REVIEW 4.4. Spring and Autumn Seasons 14 of 19 In the spring and because autumnof seasons, thedemand summerpattern or winter seasons, thestate. flowArate was high are comparably small a fairlyunlike constant in the current comparison during the daytime and continuous in the evening, as shown in Figure 10. of energy costs is summarized in Table 8. Figure 10. Result season. Figure 10. Result comparison comparison for for the the spring-autumn spring-autumn season. Table 8. Hourly variations of the flow rate pattern for the spring–autumn season. In the spring and autumn seasons, the shapes of the curves in Figure 10 are very different between HourlyInFlow Rate the current state and the simulation cases. the current case, no significantDifference variationwith is observed 3/h) 3) (m Current Time Cost Level between 9:00 a.m. and 9:00 p.m., while the simulation cases show an hourly varying (m curve as high Current F1 F2 F3 FGA F1 F2 F3 FGAS3, demand during the morning and evening. F1, F2, and F3 show a similar variation as the S1, S2, and 1 1097 1532 1758 1985 1125 −435 −662 −888 −28 respectively. However, the F1 and F2 cases do not show the reversed demand pattern in the morning 2 861 1375 1602 1828 1284 −514 −741 −967 −423 and evening, while F3 shows a reversed pattern from the morning to evening. In terms of the FGA 3 752 1288 1515 1741 1298 −536 −762 −989 −545 case with GA optimization, the is quite 1435 close to the since the−548 hourly−775 energy−1001 cost in spring, 4 660 curve1208 1661SGA 1336 −676 summer, differences state SGA are comparably 5 and autumn Low is constant. 690 The1234 1460 between 1686 the current 1156 −544 and−770 −996 −466 small 6because of a fairly constant current1179 state. A−544 comparison 694 demand 1238 pattern 1464in the 1691 −770 of energy −997 costs −485 is summarized in Table 8. 7 869 1382 1609 1,835 1594 −513 −739 −966 −725 8 shown in Table 8, the 1191 1623water 1850 2076 1605 to the −432 −658 is higher −885 in−414 As transferred from the mid hours low hours cases 9 1911 2052 2278 2504 2206 −141 −367 −594 −296 F1, F2, and F3 than in FGA, while the transferred water from the peak hours to the low hours is higher 1537 to1375 283 445 107 in in the10FGA case Mid than in the 1982 rest of the1699 cases, similar the case1875 for the summer season. 606 Therefore, 11 1885 1782 1620 1459 1644 103 265 426 241 the cases for the winter season, the FGA case shows higher efficiency for energy savings. Figure 11 12 Peak 1925 1681 1519 1357 1714 244 406 567 210 shows the transferred water demand from the peak and mid hours to the low hours. 13 14 15 16 17 18 19 20 21 22 23 Mid Peak Mid 2204 1987 1904 1865 1910 2014 1816 1866 2157 2080 1846 1789 1664 1592 1554 1590 1578 1613 1639 1751 1621 1449 1627 1502 1430 1392 1428 1416 1451 1478 1589 1459 1288 1465 1341 1269 1230 1266 1254 1290 1316 1428 1298 1126 1588 1667 1309 1334 1312 1348 1608 2126 1605 2139 1617 415 323 312 311 320 437 204 227 406 458 397 577 484 474 473 482 598 365 389 568 620 558 738 646 635 635 644 760 527 551 729 782 720 616 320 595 531 598 667 209 −259 552 −60 228 Sustainability 2018, 10, 1118 15 of 19 Table 8. Hourly variations of the flow rate pattern for the spring–autumn season. Time Hourly Flow Rate (m3 /h) Cost Level Difference with Current (m3 ) Current F1 F2 F3 FGA F1 F2 F3 FGA 1532 1375 1288 1208 1234 1238 1382 1623 2052 1758 1602 1515 1435 1460 1464 1609 1850 2278 1985 1828 1741 1661 1686 1691 1,835 2076 2504 1125 1284 1298 1336 1156 1179 1594 1605 2206 −435 −514 −536 −548 −544 −544 −513 −432 −141 −662 −741 −762 −775 −770 −770 −739 −658 −367 −888 −967 −989 −1001 −996 −997 −966 −885 −594 −28 −423 −545 −676 −466 −485 −725 −414 −296 1 2 3 4 5 6 7 8 9 Low 1097 861 752 660 690 694 869 1191 1911 10 11 Mid 1982 1885 1699 1782 1537 1620 1375 1459 1875 1644 283 103 445 265 606 426 107 241 12 Peak 1925 1681 1519 1357 1714 244 406 567 210 13 Mid 2204 1789 1627 1465 1588 415 577 738 616 Peak 1987 1904 1865 1910 1664 1592 1554 1590 1502 1430 1392 1428 1341 1269 1230 1266 1667 1309 1334 1312 323 312 311 320 484 474 473 482 646 635 635 644 320 595 531 598 14 15 16 17 18 2014 1578 19 1816 1613 Sustainability 2018, 9, x FOR PEER1866 REVIEW 1639 20 Mid 21 2157 1751 22 2080 Peak 9591 1621 8081 23 1846 1449 1416 1254 1348 437 598 1451 1290 1608 204 365 1478 1316 2126 227 389 1589 1428 1605 406 568 14597272 1298 64632139 7336458 1510 620 2319 1288 1126 1617 397 558 760 527 551 729 7823127 720 667 209 −15 259of 19 552 −2255 60 228 24 1497 1731 water 1957 from 2184 1996hours −234 −461 As shown Low in Table 8, the transferred the mid to the low hours − is687 higher − in499 cases F1, F2, and F3 than while the transferred the peak hours−to the low hours is Lowin FGA,10,222 14,663 16,927 water 19,191from 14,780 −4441 6705 −8969 −higher 4558 13,466 12,010 4385 season. 5840Therefore, 2301 in 17,851 in Summary the FGA caseMid than in the rest of 14,921 the cases, similar to the15,550 case for2929 the summer Peak 9591 the8081 7272shows 6463higher 7336efficiency 1510 for energy 2319 3127 Figure 2255 11 the cases for the winter season, FGA case savings. shows the transferred water demand from the peak and mid hours to the low hours. Figure season simulation. simulation. Figure 11. 11. Flow Flow rate rate transferred transferred to to the the low low cost cost hours hours in in the the spring–autumn spring–autumn season In Figure 11, the water demand transferred from the peak hours to the low hours in cases F1, F2, and F3 were approximately 35%, while it was approximately 50% in the FGA case, as the GA tries to give priority for water demand during peak hours. The saving effect in cases W(1, 2, and 3) and FGA was approximately 30%. Sustainability 2018, 10, 1118 16 of 19 In Figure 11, the water demand transferred from the peak hours to the low hours in cases F1, F2, and F3 were approximately 35%, while it was approximately 50% in the FGA case, as the GA tries to give priority for water demand during peak hours. The saving effect in cases W(1, 2, and 3) and FGA was approximately 30%. 4.5. Energy Cost Savings As discussed in the previous chapter, only 37.6% of the storage facilities are being used in the current state. Therefore, the realistic, available capacity to be used for energy cost optimization is approximately 60% of total storage facilities, and only the S1, W1, and F1 cases could be considered close to the real world scenario. The current seasonal simulations (S1, W1, and F1) and optimization cases (SGA, WGA, and FGA) compared in this chapter are summarized in Table 9. Table 9. Comparison of energy cost by current state (base) and simulation cases for each season. Summer Season Hour Cost (USD) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Sum 0.059 0.059 0.059 0.059 0.059 0.059 0.059 0.059 0.059 0.112 0.112 0.193 0.112 0.193 0.193 0.193 0.193 0.112 0.112 0.112 0.112 0.112 0.112 0.059 Reduce Rate (%) Winter Season Base S1 SGA 92 84 80 59 46 45 53 66 85 198 208 345 202 315 295 302 305 168 157 174 177 187 184 86 3915 94 85 80 69 70 71 77 88 103 168 173 303 176 308 287 279 270 157 161 166 165 156 152 92 3750 82 82 77 78 61 73 92 82 112 216 154 296 155 310 232 226 216 130 158 190 149 199 192 103 3667 4.22 6.33 Cost (USD) 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.110 0.166 0.166 0.110 0.110 0.110 0.110 0.110 0.166 0.166 0.166 0.110 0.110 0.166 0.065 Spring–Autumn Base W1 WGA 103 99 93 89 94 87 97 131 121 214 353 309 212 210 210 214 188 285 311 351 280 263 406 153 4873 126 122 117 114 114 118 128 139 142 201 309 300 206 204 193 188 189 294 304 314 210 209 297 137 4675 126 123 123 114 105 130 135 112 137 244 277 290 218 223 174 175 177 245 316 309 204 256 283 150 4645 4.06 4.69 Cost (USD) 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.110 0.166 0.166 0.110 0.110 0.110 0.110 0.110 0.166 0.166 0.166 0.110 0.110 0.166 0.065 Base F1 FGA 65 51 44 39 41 41 51 70 113 163 155 216 182 223 214 210 215 166 150 154 178 171 152 88 3153 91 81 76 71 73 73 82 96 121 140 147 189 147 187 179 175 179 130 133 135 144 134 119 102 3004 66 76 77 79 68 70 94 95 130 155 135 193 131 187 147 150 147 111 132 175 132 176 133 118 2979 4.71 5.51 In terms of the energy cost in the summer season, the cost reductions would be 4.22% for S1, 6.33% for SGA, 4.06% for W1, and 4.69% for WGA from the 3.92 USD/m3 cost at the current state. For spring and autumn, F1 and FGA can be reduced by 4.71% and 5.51%, respectively. It means that the energy efficiency reduction is best in the summer season, while the winter season does not show significant differences. In this study, only WDNs were applied to reduce the energy cost in a water supply system; however, the cost reduction ratio could be applied to the whole process in the water supply system, including the water supply pump from the WTP to the reservoir, the intake pump, and the boost pump from the intake to the WTP. The monthly reduction ratio is determined as the reducing ratio of energy cost from SGA, WGA, and FGA, and the estimated reductions are summarized in Table 10. Sustainability 2018, 10, 1118 17 of 19 Table 10. Estimated reduction of energy cost. Estimated Reduction (US Dollar) Pumping Energy Cost (US Dollar) Month Water treatment plant (WTP) to Reservoir Intake to WTP Intake Pump 1 2 3 4 5 6 7 8 9 10 11 12 Total 217,187 204,927 163,857 158,186 161,967 162,426 208,737 231,201 178,433 187,814 220,807 224,347 2,319,890 374,619 368,949 295,386 284,807 265,562 277,781 363,752 392,057 288,754 314,747 359,276 343,844 3,929,535 198,833 195,837 159,691 152,206 143,691 150,808 194,557 209,807 157,592 177,283 201,406 189,648 2,131,360 Reduction Rate 4.69 4.69 5.51 5.51 5.51 5.51 6.33 6.33 5.51 5.51 4.69 4.69 - WTP to Reservoir Intake to WTP Intake Pump 10,186 9611 9029 8716 8924 8950 13,213 14,635 9832 10,349 10,356 10,522 124,322 17,570 17,304 16,276 15,693 14,632 15,306 23,026 24,817 15,910 17,343 16,850 16,126 210,852 9325 9185 8799 8387 7917 8310 12,315 13,281 8683 9768 9446 8894 114,311 5. Conclusions In a water distribution system, the proportion of energy costs from using pumps at the WDN is high for the total cost. Energy cost (electricity) varies according to the season of year and the hours of a day. The water demand pattern is similar to the hourly energy cost curve, so high water demand occurs during high energy cost hours. In this study, under the assumption that energy cost reduction is possible through redistributing the demand pattern, a numerical analysis was conducted on transferring the water demand at peak energy cost hours to low energy cost hours by the storage facilities. This study was applied to a real facility, the Bupyeong 2 reservoir catchment, and produced the following conclusions. The demand pattern was optimized using several methods, and the optimization was applied for the summer, winter, and spring–autumn seasons. The maximum energy cost savings from the optimization was 6.33% for the summer season. Only 37.6% of the total capacity of the storage facilities was being used, and 60% of storage capacity was still available for this study. This study confirms that it is possible to reduce energy costs by using electricity during the low-cost hours to fill storage facilities to be used during peak hours. In this study, the real capacity of the storage facilities in the study area was applied to redistribute the water demand from the peak hours to the low hours. The result of the energy cost reduction could be generalized throughout the water supply system and applied to the major procedures involved, such as pumping the water supply from the water treatment plant to the reservoir, using the water intake pump, and powering the boost pump to move water from the intake to the water treatment plant. In total, approximately 5.36% of energy cost could be reduced. This study applied water demand patterns, pipe networks, storage facilities, and hourly varying electricity prices in a study area without special characteristics. An energy cost changes over time in many regions of the world and there are many water storage facilities that are not actively being used. Therefore, it is possible to apply this research to the other regions as a worldwide application for studies on energy savings, improvement of the water billing system or smart water grids. In addition, we studied energy costs and water demand among the water supply costs and, in further study, water tariffs can be an additional variable for energy savings since the low price of water tariffs can trigger over use of water. Acknowledgments: This research was supported by the National Strategic Project-Carbon Upcycling of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), the Ministry of Environment (ME) and the Ministry of Trade, Industry and Energy (MOTIE) (2017M3D8A2090376). 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